Voronoi binding site models

A frequently occurring problem in drug design and enzymology is that the binding constants for several compounds to the same site are known, but the geometry and energetic interactions of the site are not. This paper presents in detail a novel approach to the problem which accurately but compactly represents the allowed conformation space of each ligand, accurately depicts their three-dimensional structures, and realistically allows each ligand to adopt the conformation and positioning in the site which is most favorable energetically. The investigator supplies only the ligand structures and observed binding free energies, along with a proposed site geometry. With no further assumptions about how the ligands bind and what parts of the ligands are important in determining the binding, the algorithm fits the observed binding energies without leaving outliers, predicts exactly how each of the given ligands binds in the site, and predicts the strength and mode of binding of new compounds, regardless of chemical similarity to the original set of ligands. The method is illustrated by devising a simple site that accounts for the binding of five polychlorinated biphenyls to thyroxine binding prealbumin. This model then predicts the binding energies correctly for an additional six biphenyls, and fails on one compound.     

A general technique to rank protein-ligand binding affinities and determine allosteric versus direct binding site competition in compound mixtures

To realize the full potential of combinatorial chemistry-based drug discovery, generic and efficient tools must be developed that apply the strengths of diversity-oriented chemical synthesis to the identification and optimization of lead compounds for disease-associated protein targets. We report an affinity selection-mass spectrometry (AS-MS) method for protein-ligand affinity ranking and the classification of ligands by binding site. The method incorporates the following steps: (1) an affinity selection stage, where protein-binding compounds are selected from pools of ligands in the presence of varying concentrations of a competitor ligand, (2) a first chromatography stage to separate unbound ligands from protein-ligand complexes, and (3) a second chromatography stage to dissociate the ligands from the complexes for identification and quantification by MS. The ability of the competitor ligand to displace a target-bound library member, as measured by MS, reveals the binding site classification and affinity ranking of the mixture components. The technique requires no radiolabel incorporation or direct biochemical assay, no modification or immobilization of the compounds or target protein, and all reaction components, including any buffers or cofactors required for protein stability, are free in solution. We demonstrate the method for several compounds of wide structural variety against representatives of the most important protein classes in contemporary drug discovery, including novel ATP-competitive and allosteric inhibitors of the Akt-1 (PKB) and Zap-70 kinases, and previously undisclosed antagonists of the M(2) muscarinic acetylcholine receptor, a G-protein coupled receptor (GPCR). The theoretical basis of the technique is analyzed mathematically, allowing quantitative estimation of binding affinities and, in the case of allosteric interaction, absolute determination of binding cooperativity. The method is readily applicable to high-throughput screening hit triage, combinatorial library-based affinity optimization, and developing structure-activity relationships among multiple ligands to a given receptor.     

The discovery of novel auxin transport inhibitors by molecular modeling and three-dimensional pattern analysis

Summary Molecular modeling techniques and three-dimensional (3D) pattern analysis have been used to investigate the chemical and steric properties of compounds that inhibit transport of the plant hormone auxin. These compounds bind to a specific site on the plant plasma membrane characterized by its affinity for the herbicide N-1-naphthylphthalamic acid (NPA). A 3D model was derived from critical features of a set of ligands for the NPA receptor, a suggested binding conformation is proposed, and implications for the topographical features of the NPA receptor are discussed. This model, along with 3D structural analysis techniques, was then used to search the Abbott corporate database of chemical structures. Of the 467 compounds that satisfied the criteria of the model, 77 representative molecules were evaluated for their ability to compete for the binding of [³H]NPA to corn microsomal membranes. Nineteen showed activity that ranged from 16 to 85% of the maximum NPA binding. Four of the most active of these, representing chemical classes not included in the original compound set, were also found to inhibit polar auxin transport through corn coleoptile sections. Thus, this study demonstrates that 3D analysis techniques can identify active, novel ligands for biochemical target sites with concomitant physiological activity.     

Large scale free energy calculations for blind predictions of protein–ligand binding: the D3R Grand Challenge 2015

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein–ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.     

QUASI: a novel method for simultaneous superposition of multiple flexible ligands and virtual screening using partial similarity

The structure of many receptors is unknown, and only information about diverse ligands binding to them is available. A new method is presented for the superposition of such ligands, derivation of putative receptor site models and utilization of the models for screening of compound databases. In order to generate a receptor model, the similarity of all ligands is optimized simultaneously taking into account conformational flexibility and also the possibility that the ligands can bind to different regions of the site and only partially overlap. Ligand similarity is defined with respect to a receptor site model serving as a common reference frame. The receptor model is dynamic and coevolves with the ligand alignment until an optimal self-consistent superposition is achieved. When ligand conformational flexibility is permitted, different superposition models are possible and consistent with the data. Clustering of the superposition solutions is used to obtain diverse models. When the models are used to screen a database of compounds, high enrichments are obtained, comparable to those obtained in docking studies.     

A model of the human M2 muscarinic acetylcholine receptor

The M₂ muscarinic acetylcholine receptor belongs to the family of rhodopsin like G-Protein Coupled Receptors. This subtype of muscarinic receptors is of special interest because it bears, aside from an orthosteric binding site, also an allosteric binding site. Based on the X-ray structure of bovine rhodopsin a complete homology model of the human M₂ receptor was developed. For the orthosteric binding site point mutations and binding studies with different agonists and antagonists are available. This knowledge was utilized for an initial verification of the M₂ model. Allosteric modulation of activity is mediated by structurally different ligands such as gallamine, caracurine V salts or W84 (a hexamethonium-derivative). Caracurine V derivatives with different affinities to M₂ were docked using GRID-fields. Subsequent molecular dynamics simulations yielded different binding energies based on diverse electrostatic and lipophilic interactions. The calculated affinities are in good agreement to experimentally determined affinities.     

Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking,MM/GBSA,QM/MM,and free-energy simulations

We have estimated the binding affinity of three sets of ligands of the heat-shock protein 90 in the D3R grand challenge blind test competition. We have employed four different methods, based on five different crystal structures: first, we docked the ligands to the proteins with induced-fit docking with the Glide software and calculated binding affinities with three energy functions. Second, the docked structures were minimised in a continuum solvent and binding affinities were calculated with the MM/GBSA method (molecular mechanics combined with generalised Born and solvent-accessible surface area solvation). Third, the docked structures were re-optimised by combined quantum mechanics and molecular mechanics (QM/MM) calculations. Then, interaction energies were calculated with quantum mechanical calculations employing 970–1160 atoms in a continuum solvent, combined with energy corrections for dispersion, zero-point energy and entropy, ligand distortion, ligand solvation, and an increase of the basis set to quadruple-zeta quality. Fourth, relative binding affinities were estimated by free-energy simulations, using the multi-state Bennett acceptance-ratio approach. Unfortunately, the results were varying and rather poor, with only one calculation giving a correlation to the experimental affinities larger than 0.7, and with no consistent difference in the quality of the predictions from the various methods. For one set of ligands, the results could be strongly improved (after experimental data were revealed) if it was recognised that one of the ligands displaced one or two water molecules. For the other two sets, the problem is probably that the ligands bind in different modes than in the crystal structures employed or that the conformation of the ligand-binding site or the whole protein changes.     

Application of affinity selection-mass spectrometry assays to purification and affinity-based screening of the chemokine receptor CXCR4

Affinity selection-mass spectrometry (AS-MS) is a sensitive technology for identifying small molecules that bind to target proteins, and assays enabled by AS-MS can be used to delineate relative binding affinities of ligands for proteins. 'Indirect' AS-MS assays employ size-exclusion techniques to separate target-ligand complexes from unbound ligands, and target-associated ligands are then specifically detected by liquid chromatography mass spectrometry. We report how indirect AS-MS binding assays with known reference control compounds were used as guideposts for development of an optimized purification method for CXCR4, a G-protein coupled chemokine receptor, for which we sought novel antagonists. The CXCR4 purification method that was developed was amenable to scale-up and enabled the screening of purified recombinant human CXCR4 against a large combinatorial library of small molecules by high throughput indirect AS-MS. The screen resulted in the discovery of new ligands that competed off binding of reference compounds to CXCR4 in AS-MS binding assays and that antagonized SDF1α-triggered responses and CXCR4-mediated HIV1 viral uptake in cell-based assays. This report provides a methodological paradigm whereby indirect AS-MS-based ligand binding assays may be used to guide optimal integral membrane protein purification methods that enable downstream affinity selection-based applications such as high throughput AS-MS screens.     

Binding pose and affinity prediction in the 2016 D3R Grand Challenge 2 using the Wilma-SIE method

The Farnesoid X receptor (FXR) exhibits significant backbone movement in response to the binding of various ligands and can be a challenge for pose prediction algorithms. As part of the D3R Grand Challenge 2, we tested Wilma-SIE, a rigid-protein docking method, on a set of 36 FXR ligands for which the crystal structures had originally been blinded. These ligands covered several classes of compounds. To overcome the rigid protein limitations of the method, we used an ensemble of publicly available structures for FXR from the PDB. The use of the ensemble allowed Wilma-SIE to predict poses with average and median RMSDs of 2.3 and 1.4 Å, respectively. It was quite clear, however, that had we used a single structure for the receptor the success rate would have been much lower. The most successful predictions were obtained on chemical classes for which one or more crystal structures of the receptor bound to a molecule of the same class was available. In the absence of a crystal structure for the class, observing a consensus binding mode for the ligands of the class using one or more receptor structures of other classes seemed to be indicative of a reasonable pose prediction. Affinity prediction proved to be more challenging with generally poor correlation with experimental IC50s (Kendall tau?~?0.3). Even when the 36 crystal structures were used the accuracy of the predicted affinities was not appreciably improved. A possible cause of difficulty is the internal energy strain arising from conformational differences in the receptor across complexes, which may need to be properly estimated and incorporated into the SIE scoring function.     

Experimental versus predicted affinities for ligand binding to estrogen receptor: iterative selection and rescoring of docked poses systematically improves the correlation

The computational determination of binding modes for a ligand into a protein receptor is much more successful than the prediction of relative binding affinities (RBAs) for a set of ligands. Here we consider the binding of a set of 26 synthetic A-CD ligands into the estrogen receptor ERα. We show that the MOE default scoring function (London dG) used to rank the docked poses leads to a negligible correlation with experimental RBAs. However, switching to an energy-based scoring function, using a multiple linear regression to fit experimental RBAs, selecting top-ranked poses and then iteratively repeating this process leads to exponential convergence in 4–7 iterations and a very strong correlation. The method is robust, as shown by various validation tests. This approach may be of general use in improving the quality of predicted binding affinities.     

Estrogen-derived steroidal metal complexes: agents for cellular delivery of metal centers to estrogen receptor-positive cells

Targeted cellular delivery of drugs to specific tissues is an important goal in biomedical chemistry. Achieving this requires harnessing and applying molecular-level recognition events prevalent in (or specific to) the desired tissue type. Tissues rich in estrogen receptors (ERs), which include many types of breast cancer, accumulate molecules that have high binding affinities for these receptors. Therefore, molecules that (i) bind to the ER, (ii) have favorable cellular transport properties, and (iii) contain a second functionality (such as a center that may be used for diagnostic imaging or medical therapy) are exciting synthetic targets in the field of drug delivery. To this end, we have prepared a range of metallo-estrogens based on 17alpha-ethynylestradiol and examined their binding to the ER both as isolated receptor and in whole cell assays (ER positive MCF-7 cells). Estrogens functionalized with metal binding units are prepared by palladium-catalyzed cross-coupling reactions and a wide range of metal centers introduced readily. All the compounds prepared and tested exhibit effective binding to the estrogen receptor and are delivered across the cell membrane into MCF-7 cells. In the whole cell assays, despite their monocationic nature, the palladium and platinum complexes prepared exhibit similar (and even enhanced) receptor binding affinities compared to their corresponding neutral free ligands. It is unprecedented for a higher ER binding affinity to be observed for a cationic complex than for its metal-free ligand.     

Towards a rational spacer design for bivalent inhibition of estrogen receptor

Estrogen receptors are known drug targets that have been linked to several kinds of cancer. The structure of the estrogen receptor ligand binding domain is available and reveals a homodimeric layout. In order to improve the binding affinity of known estrogen receptor inhibitors, bivalent compounds have been developed that consist of two individual ligands linked by flexible tethers serving as spacers. So far, binding affinities of the bivalent compounds do not surpass their monovalent counterparts. In this article, we focus our attention on the molecular spacers that are used to connect the individual ligands to form bivalent compounds, and describe their thermodynamic contribution during the ligand binding process. We use computational methods to predict structural and entropic parameters of different spacer structures. We find that flexible spacers introduce a number of effects that may interfere with ligand binding and possibly can be connected to the low binding affinities that have been reported in binding assays. Based on these findings, we try to provide guidelines for the design of novel molecular spacers.     

Influencing receptor-ligand binding mechanisms with multivalent ligand architecture

Multivalent ligands can function as inhibitors or effectors of biological processes. Potent inhibitory activity can arise from the high functional affinities of multivalent ligand-receptor interactions. Effector functions, however, are influenced not only by apparent affinities but also by alternate factors, including the ability of a ligand to cluster receptors. Little is known about the molecular features of a multivalent ligand that determine whether it will function as an inhibitor or effector. We envisioned that, by altering multivalent ligand architecture, ligands with preferences for different binding mechanisms would be generated. To this end, a series of 28 ligands possessing structural diversity was synthesized. This series provides the means to explore the effects of ligand architecture on the inhibition and clustering of a model protein, the lectin concanavalin A (Con A). The structural parameters that were varied include scaffold shape, size, valency, and density of binding elements. We found that ligands with certain architectures are effective inhibitors, but others mediate receptor clustering. Specifically, high molecular weight, polydisperse polyvalent ligands are effective inhibitors of Con A binding, whereas linear oligomeric ligands generated by the ring-opening metathesis polymerization have structural properties that favor clustering. The shape of a multivalent ligand also influences specific aspects of receptor clustering. These include the rate at which the receptor is clustered, the number of receptors in the clusters, and the average interreceptor distance. Our results indicate that the architecture of a multivalent ligand is a key parameter in determining its activity as an inhibitor or effector. Diversity-oriented syntheses of multivalent ligands coupled with effective assays that can be used to compare the contributions of different binding parameters may afford ligands that function by specific mechanisms.     

Glycogen phosphorylase as a molecular target for type 2 diabetes therapy

The regulation of the hepatic glucose output through glycogenolysis is an important target for type 2 diabetes therapy. Glycogenolysis is catalyzed in liver, muscle and brain by tissue specific isoforms of glycogen phosphorylase (GP). Because of its central role in glycogen metabolism, GP has been exploited as a model for structure-assisted design of potent inhibitors, which may be relevant to the control of blood glucose concentrations in type 2 diabetes. Several regulatory binding sites have been identified in GP, such as the catalytic, the allosteric, and the inhibitor binding sites. Protein crystallography has contributed significant structural information on the specificity and interactions that distinguish the binding sites, and also revealed a new unexpected binding site (new allosteric site). In this review, the kinetic, crystallographic binding, and physiological studies of a number of compounds, inhibitors of GP, are described, and the essential inhibitory and binding properties of specific compounds are analyzed in an effort to provide rationalizations for the affinities of these compounds and to exploit the molecular interactions that might give rise to a better inhibitor. These studies have given new insights into fundamental structural aspects of the enzyme enhancing our understanding of how the enzyme recognizes and specifically binds ligands, that could be of potential therapeutic value in the treatment of type 2 diabetes.